Multi-Objective Biclustering: When Non-dominated Solutions are not Enough |
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Authors: | Guilherme Palermo Coelho Fabrício Olivetti de França Fernando J Von Zuben |
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Institution: | 1. Laboratory of Bioinformatics and Bioinspired Computing (LBiC), Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (Unicamp), Av. Albert Einstein – 400 – Building G2 – Room LE 14G, 13083–852, P. O. Box 6101, Campinas, S?o Paulo, Brazil
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Abstract: | The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques, such
as their impossibility of finding correlating data under a subset of features, and, consequently, to allow the extraction
of more accurate information from datasets. Given that biclustering requires the optimization of at least two conflicting
objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a few multi-objective
evolutionary algorithms for biclustering were proposed in the literature. However, these algorithms only focus their search
in the generation of a global set of non-dominated biclusters, which may be insufficient for most of the problems as the coverage
of the dataset can be compromised. In order to overcome such problem, a multi-objective artificial immune system capable of
performing a multipopulation search, named MOM-aiNet, was proposed. In this work, the MOM-aiNet algorithm will be described
in detail, and an extensive set of experimental comparisons will be performed, with the obtained results of MOM-aiNet being
confronted with those produced by the popular CC algorithm, by another immune-inspired approach for biclustering (BIC-aiNet),
and by the multi-objective approach for biclustering proposed by Mitra & Banka. |
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Keywords: | Biclustering Multi-objective optimization Multipopulation search Artificial immune systems |
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